Predictive analytics makes applications infinitely more valuable, separates software from competitors, and offers new revenue streams. But it’s also a vastly complex undertaking. Embedding accurate, effective predictive solutions in your application requires expert resources and taking the time to get it right.

Where should you start your predictive analytics journey? Follow these seven steps to add valuable insights to your application.

Step 1: Find a promising predictive use case

This is an important aspect of the project. Pick a business use case that your company already recognizes as a problem requiring a solution. Start by identifying the top three priorities of your executives and pick the one that’s most realistic to achieve in your timeframe. If you don’t have a specific use case in mind, start with a common business issue such as customer churn or late payments. You can also use the PADS framework to identify a strong predictive analytics initiative for your company.

Step 2: Identify the data you need

In many cases, the data you need for your chosen use case is either not readily available or may have quality issues. Consider using a predictive analytics tool to do auto cleansing for common data problems—but don’t feel like you have to wait for everything to align before you get started. Take an 80/20 approach: If 80 percent of your data is clean, move forward with what you have and optimize from there. You can always roll out an update after the initial release.

Step 3: Gather a team of beta testers

Beta testers are the end users of your product. If you’re working on a customer-facing project, engage directly with a few key customers/partners who will be using the predictive analytics in your application. If you have an enterprise application used by internal employees or partners, gather a core group of your top users across a variety of teams or departments. One important note: Take care to pick the right combination of people so you get a variety of feedback. Don’t just pick the customers who will agree with you on everything. If all you get is positive feedback your project will never succeed.

In addition to your end users, talk to the users who will be maintaining and deploying the predictive analytics solution in your software. Conduct focus group interviews and hands-on training sessions with your development and product management teams. If the application team isn’t on board with the solution, it will end up going nowhere.

Step 4: Create rapid proofs of concept

Create simple prototypes and get them to your end users/stakeholders for feedback. Your first few iterations will most likely be way off mark—but that’s to be expected. It’s common to take a dozen or more iterations to find the right design.

Step 5: Integrate predictive analytics in your operations

The most valuable predictive analytics solutions are integrated in existing workflows, processes, and decision making steps. Users get future insights in context of the applications they already use—and they can act on those insights without jumping into another system. As more users benefit from predictions, even more users will want to adopt the application. The key is to make it easy for end users to see predictions and take action all within the same application.

Step 6: Partner with stakeholders

Stakeholders may be skeptical of predictive analytics at first. But by doing your due diligence and gathering feedback from end users, sample prototypes, and examining the competitive landscape, you can build a business case to move your project forward. Partner with stakeholders at every step of the journey: You need them to succeed, and they need you to achieve successful outcomes as well.

Step 7: Update regularly

End users like to see new features added and bugs fixed at a reasonable pace. Plan a predictive analytics roadmap to make small updates every two to three months and significant updates every six to nine months. Maintain constant communication with end users and continuously respond to their needs to keep your project on the right path.

About the Author

Sriram Parthasarathy is the Senior Director of Predictive Analytics at Logi Analytics. Prior to working at Logi, Sriram was a practicing data scientist, implementing and advising companies in healthcare and financial services for their use of Predictive Analytics. Prior to that, Sriram was with MicroStrategy for over a decade, where he led and launched several product modules/offerings to the market.